Speaker 1: Picture this. The company website isn't technically down, but every
Speaker 1: single alarm on the operation's dashboard is just flashing red.
Speaker 2: Oh man, the absolute worst nightmare for any software developer, right.
Speaker 1: Support calls are just flooding in expensive, angry calls from
Speaker 1: frustrated customers, And the root cause is that someone made
Speaker 1: a change to a third party API and it's causing
Speaker 1: this massive data corruption on your.
Speaker 2: End, which is terrifying, especially when you consider the system
Speaker 2: talking to that API is usually some massive legacy beasts exactly.
Speaker 1: In this case, it's a five hundred thousand line Java
Speaker 1: enterprise system, and of course the one developer who actually
Speaker 1: understands it is sitting on a beach somewhere completely unreachable.
Speaker 2: Yeah, naturally, and normally, just tracking down a bug like
Speaker 2: that in a massive Java code base, compiling a fix,
Speaker 2: running tests, and deploying it, I mean that takes days.
Speaker 1: But according to the opening of the book we're exploring today,
Speaker 1: the author Michael Bevelocklyn faced this exact scenario and he
Speaker 1: fixed it in an hour.
Speaker 2: Which honestly borders on science fiction for anyone who's ever
Speaker 2: wrestled with legacy enterprise code.
Speaker 1: It really does. But The kicker is he didn't use
Speaker 1: Java to fix it. He spun up a closure RIPL
Speaker 1: to bypass all those usual deployment delays. And we should
Speaker 1: probably explain what that is, because it's totally crucial to
Speaker 1: understanding how you move so fast.
Speaker 2: Yeah. So RPL stands for read evil print loop. It's
Speaker 2: this interactive programming environment. So instead of the slow traditional
Speaker 2: Java cycle where you write code, compile the whole massive project,
Speaker 2: restart the server, and just cross your fingers, which takes forever, right,
Speaker 2: it takes forever, But a RPL lets you interact with
Speaker 2: a live running system. You type a command, the environment
Speaker 2: evaluates it instantly against the live data, and it prints
Speaker 2: the result right back to you.
Speaker 1: So he used that live immediate connection to the data
Speaker 1: to write a custom domain specific language and then wrote
Speaker 1: a functional data transformation to clean up the corrupted mess instantly.
Speaker 2: It's brilliant And.
Speaker 1: A week later, at an all hands meeting, the boss
Speaker 1: thanked him for the Java program that saved the day,
Speaker 1: and he just smiled and said, that wasn't Java. I
Speaker 1: love that start so good. So welcome to the deep dive.
Speaker 1: Today we were exploring Bevel Acolen's book Functional programming patterns
Speaker 1: in Skala enclosure, and the mission of this deep dive
Speaker 1: is to help you cross the bridge from the traditional,
Speaker 1: kind of bulky, object oriented programming world into the lean,
Speaker 1: highly expressive world of functional programming.
Speaker 2: Yeah, because functional languages like Scala enclosure, they fundamentally flip
Speaker 2: how we structure software. I mean, they treat functions as
Speaker 2: first class citizens. They strictly favor immutability over changing state,
Speaker 2: and they're inherently declarative.
Speaker 1: Okay, let's unpack this because if you're listening to this
Speaker 1: and wondering why you should even care about a programming
Speaker 1: paradigm shift, it's really about eliminating the sheer exhaustion of
Speaker 1: writing endless boilerplate code. Absolutely, it's about understanding how modern
Speaker 1: JVM applications are evolving to handle incredible scale without crashing.
Speaker 1: So let's start with how these two paradigms handle aptions.
Speaker 1: We're essentially moving from a world ruled by nouns to
Speaker 1: a world power by verbs.
Speaker 2: Right, in strict object oriented programming, everything has to be
Speaker 2: an object. Everything is a noun. So verbs, you know,
Speaker 2: the actual actions, the functions that do the work, they're
Speaker 2: kind of trapped.
Speaker 1: You're trapped in the classes.
Speaker 2: Exactly. They can't exist on their own. They have to
Speaker 2: be permanently attached to a class.
Speaker 1: The book uses this hilarious real world analogy to point
Speaker 1: out the absurdity of this, specifically looking at the functional
Speaker 1: interface pattern in Java. Like, imagine if you couldn't just
Speaker 1: run because run is a verb.
Speaker 2: Right, you can't just do the thing.
Speaker 1: Yeah, Instead, to execute the action of going for a run,
Speaker 1: you would have to invent like a runner object, and
Speaker 1: a shoe putter on her object, and a door opener object.
Speaker 2: Which sounds ridiculous when you say it out loud, but
Speaker 2: we do this constantly in traditional object oriented languages, I
Speaker 2: really do. Like, if you want to pass a single
Speaker 2: simple action around, say just a custom sorting rule, you
Speaker 2: can't just pass the rule itself. You have to wrap
Speaker 2: it in a class. You write five lines of boilerplate
Speaker 2: code just to create a comparator class, instantiate an object
Speaker 2: of that class, and then pass that object into your
Speaker 2: sorting algorithm.
Speaker 1: It's a massive amount of syntactic overhead just to tell
Speaker 1: the computer, hey, sort these people by their first name
Speaker 1: instead of their last name.
Speaker 2: Exactly. So functional programming relies on higher order and anonymous
Speaker 2: functions to strip all that.
Speaker 1: Away, which is where the first class citizen thing comes in,
Speaker 1: right right, When we.
Speaker 2: Say a function is a first class citizen, it just
Speaker 2: means the language treats the function exactly the same way
Speaker 2: it treats a string or an integer. You don't need
Speaker 2: a wrapper class. You just write a tiny anonymous function
Speaker 2: right there in line and pass it directly into the
Speaker 2: sorting algorithm.
Speaker 1: You've turned five lines of class bureaucracy into one line
Speaker 1: of pure logic. Yes. I am going to push back
Speaker 1: on this though, because classes do more than just hold methods, right,
Speaker 1: They hold state, They have properties and variables. So if
Speaker 1: a function is just a flow vading verb in the ether,
Speaker 1: how does it remember anything? Don't we lose the context
Speaker 1: that a class provides.
Speaker 2: Well, what's fascinating here is the mechanism functional language is
Speaker 2: used to solve that exact problem. It's called a closure.
Speaker 1: A closure.
Speaker 2: Yeah, when you create a function in Scala or closure,
Speaker 2: it automatically bundles itself with the scope and context was
Speaker 2: created in it physically like closes over the variables that
Speaker 2: were present in memory at the exact moment of its birth.
Speaker 1: Oh wow, so it's basically carrying an invisible backpack of data.
Speaker 1: Wherever it goes, it remembers what it needs without us
Speaker 1: wiring up class properties and constructors.
Speaker 2: That's a great way to put it. In fact, the
Speaker 2: text mentions this classic programmer joke. Classes are a poor
Speaker 2: man's closure and closures are a poor man's class ah,
Speaker 2: because they essentially solve the exact same problem of marine
Speaker 2: state and behavior. The difference is that closures achieve it
Speaker 2: with virtually zero extra curd.
Speaker 1: Wait, if a closure can hold on to state without
Speaker 1: needing a class, does that completely invalidate? How how we
Speaker 1: design larger enterprise patterns like I'm looking at you command pattern?
Speaker 2: Oh? The command pattern is a perfect casualty of this shift.
Speaker 2: I mean, in enterprise software you use the command pattern
Speaker 2: to turn a method invocation into an object, usually so
Speaker 2: you can queue it up, log it, or undo it later.
Speaker 1: Write the classic undue button exactly.
Speaker 2: But traditional object oriented design requires a massive bureaucracy to
Speaker 2: make it happen. You need a client to create the command,
Speaker 2: an invoker to run it, a receiver to act on it,
Speaker 2: and a command interface to tie all those pieces together.
Speaker 1: And the book illustrates this with a cash register example.
Speaker 2: Right, yes, it does.
Speaker 1: In Java, just to record a simple purchase and keep
Speaker 1: a history for those undue capabilities, you have to build
Speaker 1: a purchase command class, a cash register receiver class, and
Speaker 1: a purchase invoker class. I mean it feels like you're
Speaker 1: setting up a corporate committee just to buy a cup
Speaker 1: of coffee.
Speaker 2: It really does. But in Skala, because of closures and
Speaker 2: higher order functions, you bypass the interface and the invoker entirely.
Speaker 2: You just write a simple make.
Speaker 1: Purchase function just a function, just a function.
Speaker 2: It takes the register and the purchase amount, and it
Speaker 2: returns another function that performs the actual addition.
Speaker 1: And because that return function is a closure, its invisible
Speaker 1: backpack remembers the purchase amount and the specific register it's
Speaker 1: supposed to act.
Speaker 2: On exactly, and to keep track of the history and
Speaker 2: execution function just takes that returned closure and drops it
Speaker 2: into a standard list before running it. Boom, you have
Speaker 2: your history log.
Speaker 1: That's amazing, and closure strips it down even further, doesn't it.
Speaker 2: Oh yeah, you don't even need a cash register object.
Speaker 2: Closure uses a concept called an atom to manage the
Speaker 2: total an atom, yeah, and atom is a functional construct
Speaker 2: specifically designed to manage a piece of changing states safely,
Speaker 2: so you don't build a class around it. You just
Speaker 2: manipulate the atom directly with your purchase functions.
Speaker 1: So if someone is listening to this and wondering what
Speaker 1: the actual day to day impact is for them, it
Speaker 1: really comes down to cognitive load. Com Lately, you are
Speaker 1: getting enterprise grade undue replay and logging capabilities, but with
Speaker 1: a fraction of the code. Like when you open that
Speaker 1: file six months from now, you aren't hunting through four
Speaker 1: different class files just to figure out what a purchase does.
Speaker 1: It's all right there, localized and just vastly.
Speaker 2: Easier to read, right, because we have essentially replaced heavy
Speaker 2: classes with lightweight functions and closures. But you know that
Speaker 2: naturally brings up the data itself. What are these functions
Speaker 2: actually operating on? Well, in functional programming, data doesn't change.
Speaker 2: It is strictly immutable. Once a piece of data is created,
Speaker 2: it is locked in stone forever.
Speaker 1: Which, coming from the object oriented world, that concept can
Speaker 1: sound incredibly restrictive. I mean, in Java, building truly immutable
Speaker 1: objects is painful. It leads to the whole telescoping constructor problem.
Speaker 2: Oh yeah, you see this all the time you have
Speaker 2: a person object, sometimes you only have a first and
Speaker 2: last name. Sometimes you have a middle name or a title.
Speaker 2: So you end up writing like ten different constructors to
Speaker 2: cover all the possible combinations of defaults, or you abandon
Speaker 2: constructors entirely and use the fluent builder pattern.
Speaker 1: Right, and the builder pattern requires you to create a
Speaker 1: whole separate builder class with individual methods to set each property,
Speaker 1: which finally returns the lockdown immutable object. Trying to keep
Speaker 1: data immutable in Java is like trying to build a
Speaker 1: ship in a bottle using tweezers.
Speaker 2: That is painfully accurate.
Speaker 1: Yes, it's beautiful and totally secure once it's done, but
Speaker 1: the construction process is just absolutely exhausting.
Speaker 2: Well, Scala bypasses this entirely with something called case classes.
Speaker 2: Case classes give you named parameters and default arguments right
Speaker 2: out of the box. So if you want a person
Speaker 2: to have a default empty middle name, you declare it
Speaker 2: exactly once in the class definition and the compiler handles
Speaker 2: the rest, no extra constructors needed.
Speaker 1: Okay, but if the data is completely immutable, what happens
Speaker 1: when that person gets married and changes their last name? Right, Like,
Speaker 1: we can't mutate the object.
Speaker 2: We already have, right, so you use the built in
Speaker 2: dot copy method. It instantly clones the existing case class
Speaker 2: in memory, but it allows you to pass send just
Speaker 2: the new attribute you want to change.
Speaker 1: Oh that's clever.
Speaker 2: Yeah, it generates a brand new updated person object, leaving
Speaker 2: the original completely untouched. It's lightning fast and completely safe.
Speaker 1: I know Scale relies heavily on its static type system
Speaker 1: to enforce those case classes, but Closure is completely different structurally. Right,
Speaker 1: it doesn't use case classes at all, does it.
Speaker 2: No. Closure embraces dynamic simplicity. So for a lot of
Speaker 2: exploratory programming, Closure relies on simple maps, which are essentially
Speaker 2: just generic key value pairs in memory. But when you
Speaker 2: need to lock that data down, perhaps to ensure certain
Speaker 2: fields exist for polymorphism, Closure seamlessly transitions those maps into records.
Speaker 1: Wait, how does that transition actually work out of the hood,
Speaker 1: Like from a map to a record.
Speaker 2: It's quite elegant. Actually, a map is just a generic
Speaker 2: hash table. But when you define a record enclosure, the
Speaker 2: compiler generates a fast, concrete Java class with specific fields
Speaker 2: for your data. Yet to the rest of your closure code,
Speaker 2: that record still behaves exact like a standard map.
Speaker 1: So you get the strict memory structure of a class
Speaker 1: with the fluid flexibility of a dynamic map exactly. But so,
Speaker 1: why go through all this effort, why make immutability such
Speaker 1: a foundational pillar of these languages in the first place.
Speaker 2: Well, if we connect this to the bigger picture, immutability
Speaker 2: isn't just some functional purity exercise for academics. It solves
Speaker 2: one of the most expensive system crashing problems in modern
Speaker 2: software development, race conditions.
Speaker 1: Because our modern world runs on multiicore processors, we have
Speaker 1: dozens of threads running simultaneously within a single application.
Speaker 2: Exactly, A massive class of bugs occurs when thread A
Speaker 2: is trying to read a piece of data and thread
Speaker 2: B unexpectedly mutates that same data in the background. It
Speaker 2: leads to corrupted states and just unpredictable crashes. But by
Speaker 2: enforcing strict immutability, that entire class of bugs just ceases
Speaker 2: to exist. You can share your data across a thousand
Speaker 2: concurrent threads safely because you know with absolute certainty that
Speaker 2: none of them possess the ability to alter it.
Speaker 1: Knowing our data is safe from sneaky background changes is
Speaker 1: a huge relief. But I mean it introduces a major
Speaker 1: mechanical problem. If our data is immutable and we cannot
Speaker 1: mutate state, how do we loop?
Speaker 2: Yes. This is often the biggest mental hurdle for developers
Speaker 2: moving from object oriented to functional programming.
Speaker 1: Because if you think about a traditional forour loop or
Speaker 1: a four reach loop in Java under the hood, those
Speaker 1: loops relying on an iterator. They rely on mutating a
Speaker 1: state variable like iplus plus just to keep track of
Speaker 1: their exact position in a list. If we can't mutate state,
Speaker 1: we can't iterate like that.
Speaker 2: We have to shift our thinking from imperative iteration to
Speaker 2: declarative transformation. Instead of writing code that micromanages every single
Speaker 2: step of a loop, we declare what we want the
Speaker 2: final outcome to be.
Speaker 1: Okay, let's use the text specific scenario here to ground
Speaker 1: this the lambda bar and grill. We have a massive
Speaker 1: database list of people. We're opening a new restaurant, and
Speaker 1: we want to send grand opening ands, but only to
Speaker 1: the people who live in specific nearby zip codes. Right.
Speaker 2: So, in an imperative object oriented language, you tell the
Speaker 2: computer exactly how to do this. You create a temporary
Speaker 2: empty mutable list, You set up a loop counter, You
Speaker 2: check person one zip code if it matches, you mutate
Speaker 2: your temporary list by appending them to it, You increment
Speaker 2: your counter, you check person two.
Speaker 1: It's incredibly tedious, very.
Speaker 2: But in functional programming we use sequence comprehensions.
Speaker 1: Let's break down how Scala does this, because it reads
Speaker 1: almost like natural English. Use a Scala for comprehension, but
Speaker 1: it is not a traditional loop. You declare that you
Speaker 1: are pulling people from the original list, and then you
Speaker 1: embed a guard directly inside the comprehension.
Speaker 2: And the guard is basically an inline if statement, right
Speaker 2: like if zip code is close.
Speaker 1: Yeah, exactly. That guard seamlessly filters the original list and
Speaker 1: yields a brand new, immutable list containing only the matching
Speaker 1: people you didn't manage a single counter or a temporary array.
Speaker 2: Enclosure approaches it similarly, but he uses a macro closure
Speaker 2: pairs a four macro with a when clause. The logic
Speaker 2: is identical, give me the people from this sequence when
Speaker 2: this zip code condition is met, which.
Speaker 1: Is so clean. So if we're asking what the day
Speaker 1: to day impact is for a developer, it's about elevating
Speaker 1: your code from low level micromanagement to high level delegation.
Speaker 1: You start thinking in terms of data pipelines. You filter, map,
Speaker 1: and reduce rather than holding the computer's hand through every
Speaker 1: microstep of constructing a list.
Speaker 2: And it drastically reduces off by one errors.
Speaker 1: Definitely, I love an elegant data pipeline. But let's bring
Speaker 1: this back down to the messy real world. Because data
Speaker 1: pipelines are great when the data is perfect. But what
Speaker 1: happens when that sequence comprehension looks for a zip code
Speaker 1: and the zip code field is simply missing?
Speaker 2: Ah, dealing with nothing. Tony Hohore, the computer scientist who
Speaker 2: created the null reference, famously called it his billion dollar mistake.
Speaker 1: Wow, billion dollar mistake.
Speaker 2: Yeah, because it literally causes billions of dollars in crashes
Speaker 2: and debugging time across the industry. I mean, in Java,
Speaker 2: null is a constant, lingering threat. Your code becomes this
Speaker 2: labyrinth of defensive null checks. If person is not null,
Speaker 2: and if addresses not null, and if zip code is
Speaker 2: not null, it's exhausting.
Speaker 1: Or to avoid that labyrinth, object oriented developers rely on
Speaker 1: the null object pattern. You create a dummy class like
Speaker 1: a null person, and you hardcode their name is John
Speaker 1: Do and their zip code is zero zero zero zero zero.
Speaker 1: Just to ensure the program doesn't explode when it attempts
Speaker 1: to call a method on an empty reference.
Speaker 2: It works, but again it requires massive overhead. Scala takes
Speaker 2: a deeply structural approach to this problem with its option type.
Speaker 1: I find option fascinating because it acts like a type
Speaker 1: safe net. Instead of a method of returning a string
Speaker 1: that might secretly be a ticking time bomb of null,
Speaker 1: it returns an option container, and.
Speaker 2: The physical mechanism of the compiler here is key. The
Speaker 2: option container explicitly holds either some value or it holds none.
Speaker 2: Because of how Scala's static type system works, it physically
Speaker 2: prevents your code from if you try to use the
Speaker 2: value inside the option without explicitly handling the possibility that
Speaker 2: it might.
Speaker 1: Be none, you cannot accidentally ignore it exactly.
Speaker 2: You extract the data using a method like get ls,
Speaker 2: you tell the program give me the zip code, or
Speaker 2: else give me this default string. It completely eliminates those
Speaker 2: surprise null pointer exceptions that bring down servers at two
Speaker 2: in the morning, which is huge.
Speaker 1: But closure takes a much more dynamic, relaxed approach to
Speaker 1: missing data. Right enclosure nil isn't a program crashing time bomb.
Speaker 1: It simply evaluates to false in logic checks, so an
Speaker 1: if stable won't explode if it encounters a nil correct.
Speaker 2: And when you look up a value in a closure map,
Speaker 2: you use the get function. The get function inherently allows
Speaker 2: you to supply a default value right there as the
Speaker 2: third argument. So you write, you know, get person call
Speaker 2: on zip code zero zero zero zero zero. If the
Speaker 2: zip code is missing or nil, the function smoothly returns
Speaker 2: zero zero zero zero zero zero. No null object pattern
Speaker 2: or dummy classes needed.
Speaker 1: This perfectly illustrates a point from Peter Norvig, the legendary
Speaker 1: computer scientist at Google which the book references.
Speaker 2: Oh, his insight is profound. He pointed out that highly
Speaker 2: expressive languages actually make classic design patterns invisible.
Speaker 1: You don't need to write a complex visitor pattern or
Speaker 1: a null object pattern or a command pattern because the
Speaker 1: language itself handles extensibility, missing data, and function encapsulation. Natively,
Speaker 1: the pattern just kind of dissolves into the basic syntax
Speaker 1: of the language.
Speaker 2: Yeah, you stop building scaffolding and finally get to just
Speaker 2: build the building.
Speaker 1: I love that. Let's take a breath and look at
Speaker 1: the journey we've been on by diving into the concepts
Speaker 1: from Bevloquilln's book and comparing Scala and Closure to traditional Java,
Speaker 1: We've seen how to shed the heavy armor of object
Speaker 1: oriented design patterns.
Speaker 2: We've traded noun overlords for agile verbs by embracing higher
Speaker 2: order functions over bulky interfaces.
Speaker 1: We've replaced the bureaucratic command pattern with lightweight closures. We've
Speaker 1: swapped exhausting builders for elegant, immutable case classes and records.
Speaker 1: And we've elevated our loops from imperative micromanagement to declarative
Speaker 1: sequence comprehensions.
Speaker 2: And the result is code that is leaner, significantly safer
Speaker 2: from concurrency bugs, and just incredibly powerful for modern JVM applications.
Speaker 1: I want to leave you with a final provocative thought
Speaker 1: to mull Over. The text explicitly points out that Java eight,
Speaker 1: which is a massive, historically strict imperative language, introduce lambda expressions.
Speaker 1: They essentially bolted functional higher order functions onto the language
Speaker 1: via JSR three thirty five. Why because the functional programming
Speaker 1: approach is simply an undeniable win for developers.
Speaker 2: Yeah, massive strictly imperative languages are actively absorbing these functional
Speaker 2: features because they can no longer ignore the benefits.
Speaker 1: So here is the question for you. If the biggest
Speaker 1: heavyweight in the object oriented world is actively absorbing the
Speaker 1: functional programming toolkit, will the historical boundary between object oriented
Speaker 1: and functional programming eventually dissolve completely? Are we moving toward
Speaker 1: a universal high brid programming paradigm where the old tribal
Speaker 1: software wars like i'm an odev versus i'm an fp
Speaker 1: dev simply cease to exist.
Speaker 2: It's a fascinating future to consider as these tools continue
Speaker 2: to blend together.
Speaker 1: It really is. Thank you for joining us on this
Speaker 1: deep dive. Keep learning, keep questioning your assumptions, and next
Speaker 1: time you're staring at a broken diagnostic dashboard with the
Speaker 1: alarms blaring, remember our software firefighter. With the right functional tools,
Speaker 1: you might just clear those murky waters in an hour
Speaker 1: and leave everyone wondering what kind of magic you just used.